Vision-based Xylem Wetness Classification in Stem Water Potential Determination

TL;DR

This study develops a computer vision system using YOLOv8n and ResNet50 to automate stem detection and classify xylem wetness, improving the efficiency of water potential measurement in precision agriculture.

Contribution

It introduces a novel automated method combining deep learning models for stem detection and xylem wetness classification in SWP measurement.

Findings

Achieved 80.98% Top-1 accuracy in wetness classification.

Demonstrated effective automation of stem detection and water status assessment.

Enhanced precision agriculture practices through computer vision techniques.

Abstract

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.